Big Data with Risk Management

World’s data has grown exponentially into large sets of information that can only be measured in zettabytes (one zettabyte equals a billion terabytes). According to the International Data Corporation (IDC), the data was estimated to be 23 Zettabyte in 2017, and it’s expected to grow to 175 ZB by 2025.

By this time, your organization will have large sets of internal and external data which, if adequately used, will enhance its operations. It’ll become relatively easy for these institutions to mine the data and use it to predict the future of their businesses. This activity is crucial since it aids in the process of decision making, thus ensuring that every step that the organization makes is progressive and highly viable.

Big Data and Risk Management

While big data may improve the profitability of your business, you should be careful to mitigate all the risks associated with data storage and processing. Assessing the interactions of your data with your employees, Internet of Things, and vendors should offer an opportunity for you to devise a comprehensive risk management system. This will ensure that the threats of attack and data breaches are minimized, thus building confidence among all your clients.

How to Enhance Risk Management with Big Data

To help you fathom the relevance of big data in risk management, we’ll review the basic principles of mitigating risks:

If your business is handling big data, then you can be certain that you’re exposed to a significant degree of risk. Based on the definition of risk by ISO 31000 standards, it’s clear that risk is unavoidable since it arises due to uncertainties revolving around the decision-making process.

Risk management strategies are essentially developed to mitigate these uncertainties. To achieve this, you must identify the risks, evaluate their effects on your operations, prioritize, and institute measures to mitigate them. It’s necessary that you use big data analytic tools to ensure timely analysis and improve the decision-making process. 

These risk mitigation measures cut across all the industries, including retail, manufacturing, healthcare, and e-commerce. If you’re handling big data, then you should ensure that you reduce uncertain outcomes by establishing a reliable risk analysis tool.

Risk Management Applications Used with Big Data

  • Vendor Risk Management (VRM). When you’re dealing with third parties, you should expect immense challenges during audits and ISO compliance inspections. Any mishandling of the data shared to these vendors can destroy your business’s reputation. As such, it’s necessary that you apply the VRM to assess the reliability of each vendor before signing any contracts with them. You should ensure that the vendor has risk mitigation measures in place. These may include multifactor authorization and firewalls.
  • Fraud and Money Laundering Prevention. The application of predictive analytics in your risk analysis ensure that you detect fraudulent and money laundering activities early enough to counter them before they cause more harm to your business. With the sophistication of the fraudsters, it’s necessary that you keep updating your techniques. Some of the mitigation strategies that you may consider include the analysis of partner traders’ relationship profiles, unit weight analytics, and web analysis.
  • Identifying Churn. Loss of clients is one of the biggest risks for your organization! The analysis of big data in your business can help to detect churns, which will help you to institute mitigation measures. To prevent customer defections, you should ensure that your risk management tools are topnotch to guarantee privacy and confidentiality
  • Credit Management. You should regularly analyze the organization’s spending habits and repayment tendencies to ensure that you successfully manage the credit. Big data sources including airtime purchases, voucher issuance, and internet use can help in the analytics to ensure that you lessen the credit without affecting customer satisfaction
  • Operation Risk Analysis (in Manufacturing Sectors). The analysis of big data in any manufacturing company can help in monitoring the supply levels, thus guaranteeing production continuity. You can achieve this by using sensor technology for continuous and reliable analysis which protect your business from the risk of halting operations due to low supplies.  Also, you may use a real-time monitoring tool to check the production curve and detect any anomaly early enough to allow rectification. It’s estimated that by 2022, approximately 13.5 billion analysis devices will be in application globally. As such, the use of big data analytics tools can be a game changer in the manufacturing sector
  • Real Estate where Location is Key. To minimize the risk of failure, you should analyze the potential of the area you intend to set your business. This is a tricky affair! However, Starbucks has successfully used predictive technology to analyze the traffic, maps, average income, and other crucial factors that determine the potential of an area. As such, you should consider using this big data analytic technique to minimize your risks

Conclusion

Big data analysis is necessary for the growth of any business. It helps in making crucial decisions and ensuring that you adequately mitigate your risks. As such, you shouldn’t hesitate to apply any tool that improves your business profitability, enhance customer satisfaction, and safeguard your data.

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About Dequiana Jackson

Dequiana Jackson, CEO of Inspired Marketing, Inc., is a small business marketing coach who teaches women entrepreneurs how to monetize their message so they can make more money from their expertise. Dequiana is the author of Know Your Business: How to Attract Ideal Clients & Sell More and runs the award-winning blog, Entrepreneur-Resources.net.

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